An evaluation of phrasal and clustered representations on a text categorization task
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Learning structured prediction models: a large margin approach
Learning structured prediction models: a large margin approach
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Expert Systems with Applications: An International Journal
Sentiment Classification with Support Vector Machines and Multiple Kernel Functions
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Seeing several stars: a rating inference task for a document containing several evaluation criteria
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Capturing the stars: predicting ratings for service and product reviews
SS '10 Proceedings of the NAACL HLT 2010 Workshop on Semantic Search
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We propose a novel type of document classification task that quantifies how much a given document (review) appreciates the target object using not binary polarity (good or bad) but a continuous measure called sentiment polarity score (sp-score). An sp-score gives a very concise summary of a review and provides more information than binary classification. The difficulty of this task lies in the quantification of polarity. In this paper we use support vector regression (SVR) to tackle the problem. Experiments on book reviews with five-point scales show that SVR outperforms a multi-class classification method using support vector machines and the results are close to human performance.